which are displayed in Figure REF . The latent prior component represents prior beliefs over the next state's latent variable. The latent posterior \(q(z_{t+1})\)  represents a variational distribution that approximates the true posterior of the latent variable, given the observed data \(s_{t+1}\) . Finally, the reconstruction module allows to generate the next state from the corresponding latent. Overall, the model resembles a conditional VAE [1]}, trained to autoencode the next states, conditioned on current states and actions.
